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使用自然序列簇框架对PET解聚酶进行景观分析。

Landscape profiling of PET depolymerases using a natural sequence cluster framework.

作者信息

Seo Hogyun, Hong Hwaseok, Park Jiyoung, Lee Seul Hoo, Ki Dongwoo, Ryu Aejin, Sagong Hye-Young, Kim Kyung-Jin

机构信息

KNU Institute for Microorganisms, Kyungpook National University, Daegu, Republic of Korea.

Zyen Co., Daegu, Republic of Korea.

出版信息

Science. 2025 Jan 3;387(6729):eadp5637. doi: 10.1126/science.adp5637.

DOI:10.1126/science.adp5637
PMID:39745946
Abstract

Enzymes capable of breaking down polymers have been identified from natural sources and developed for industrial use in plastic recycling. However, there are many potential starting points for enzyme optimization that remain unexplored. We generated a landscape of 170 lineages of 1894 polyethylene terephthalate depolymerase (PETase) candidates and performed profiling using sampling approaches with features associated with PET-degrading capabilities. We identified three promising yet unexplored PETase lineages and two potent PETases, Mipa-P and Kubu-P. An engineered variant of Kubu-P outperformed benchmarks in terms of PET depolymerization in harsh environments, such as those with high substrate load and ethylene glycol as the solvent.

摘要

能够分解聚合物的酶已从天然来源中鉴定出来,并被开发用于塑料回收的工业用途。然而,酶优化仍有许多潜在的起点尚未被探索。我们构建了一个包含1894个聚对苯二甲酸乙二酯解聚酶(PETase)候选物的170个谱系的图谱,并使用与PET降解能力相关的特征的采样方法进行分析。我们鉴定出三个有前景但尚未被探索的PETase谱系以及两种高效的PETase,即Mipa-P和Kubu-P。Kubu-P的一种工程变体在恶劣环境(如高底物负载和以乙二醇为溶剂的环境)中的PET解聚方面优于基准。

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